Evapotranspiration is a key factor for water balance, irrigation scheduling, and crop yield. Even though, Penman-Monteith FAO-56 (PMF-56) equation had estimated the highest accuracy reference evapotranspiration (ET_o), it required complete climatic records, which may not be easily available. The present study is to develop and evaluate a gene expression programming (GEP) model for estimating mean monthly ET_o using minimal number of climatic data. Climatic variables used to estimate ET_o are maximum and minimum air temperature (T_max and T_min), mean relative humidity (RH), solar radiation (R_s), and wind speed at 2-m height (u₂). The data used in the analysis refer to 32 weather stations available at different locations in Egypt through the CLIMWAT database. The PMF-56 method was used as the reference standard for evaluating the developed GEP models based on statistical criteria such as: index of agreement (IA) and the root mean square error (RMSE). The results showed that the accuracy of the GEP model significantly improved when either RH or u₂ was used as additional input variables. The GEP model with the inputs: T_max, T_min, RH, andu₂ showed the highest IA (0.991 and 0.990) and the lowest RMSE (0.426 mm d^-1 and 0.430 mm d^-1) for training and testing sets, respectively. Comparing the results of GEP models with other empirical models showed that ET_o values estimated by using the GEP models are more accurate. Accordingly, the GEP technique can be employed successfully in modeling ET_o from the available climatic data and allowed for providing simple algebraic formulas.